Exploring Complementary Models Consisting of Machine Learning Algorithms for Landslide Susceptibility Mapping

Han Hu,Zhu Liang,Changming Wang,Ruiyuan Gao,Bailong Li

doi:10.3390/ijgi10100639

Han Hu, Zhu Liang + Show 3 more

Open Access

https://doi.org/10.3390/ijgi10100639

Copy DOI

Abstract

Landslides frequently occur because of natural or human factors. Landslides cause huge losses to the economy as well as human beings every year around the globe. Landslide susceptibility prediction (LSP) plays a key role in the prevention of landslides and has been under investigation for years. Although new machine learning algorithms have achieved excellent performance in terms of prediction accuracy, a sufficient quantity of training samples is essential. In contrast, it is hard to obtain enough landslide samples in most the areas, especially for the county-level area. The present study aims to explore an optimization model in conjunction with conventional unsupervised and supervised learning methods, which performs well with respect to prediction accuracy and comprehensibility. Logistic regression (LR), fuzzy c-means clustering (FCM) and factor analysis (FA) were combined to establish four models: LR model, FCM coupled with LR model, FA coupled with LR model, and FCM, FA coupled with LR model and applied in a specific area. Firstly, an inventory with 114 landslides and 10 conditioning factors was prepared for modeling. Subsequently, four models were applied to LSP. Finally, the performance was evaluated and compared by k-fold cross-validation based on statistical measures. The results showed that the coupled model by FCM, FA and LR achieved the greatest performance among these models with the AUC (Area under the curve) value of 0.827, accuracy of 85.25%, sensitivity of 74.96% and specificity of 86.21%. While the LR model performed the worst with an AUC value of 0.736, accuracy of 77%, sensitivity of 62.52% and specificity of 72.55%. It was concluded that both the dimension reduction and sample size should be considered in modeling, and the performance can be enhanced by combining complementary methods. The combination of models should be more flexible and purposeful. This work provides reference for related research and better guidance to engineering activities, decision-making by local administrations and land use planning.

Highlights

Introduction iationsLandslide is a common and unavoidable form of disaster, especially in mountainous areas where rainfall, earthquakes or, engineering activities frequently occurs
Four models, including the Logistic regression (LR) model, fuzzy c-means clustering (FCM) coupled with LR model, factor analysis (FA) coupled with LR model, and FCM, FA coupled with LR models, are established to compare and analyze the performance from different angles
Landslide susceptibility maps: (a) LR model; (b) FCM coupled with LR model; (c) FA coupled with LR model; (d)23FCM, Figure 8

Summary

Introduction

Landslide is a common and unavoidable form of disaster, especially in mountainous areas where rainfall, earthquakes or, engineering activities frequently occurs. The damages caused by landslides to both humans and the economy are enormous [1,2]. Landslides have attracted increasing attention and a significant amount of research has been done, especially in landslide susceptibility prediction (LSP). Damages could be avoided or decreased with limits by recognizing the likely locations of future disasters [4]. In the view of seriousness and frequency of landslide, the establishment of prediction models are indispensable and various methods have proved their effectiveness. Physical-based approaches and heuristic methods are applicable to limited samples and are time-consuming [5,6].

Methods

Results

Discussion

Conclusion